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Machine Learning Approaches for Kids’ E-learning Monitoring

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Kids Cybersecurity Using Computational Intelligence Techniques

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1080))

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Abstract

These days, online learning for kids has become essential due to the development of technology and the need for fast learning, however, online learning for kids can bring some risks to children, for example, some students find difficulties in their learning which affect on their performance, so machine learning (ML) approaches play essential roles in monitoring kids’ learning performance through online. Therefore, the aim of this chapter is to review some aspects of machine learning approaches in monitoring online learning for children and discuss the roles of machine learning (ML) methods in exam preparation and management by reviewing papers over the last five years. The steps in predicting at-risk students are explained to help kids by providing early intervention before exams. And finally, the issues and threats related to using ML in the examination system are discussed briefly.

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Authors and Affiliations

  1. Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Malaysia

    Howida Abubaker Al-kaaf

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  1. Howida Abubaker Al-kaaf
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Correspondence to Howida Abubaker Al-kaaf .

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Editors and Affiliations

  1. Computer Science Department, Taibah University, Madinah, Saudi Arabia

    Wael M. S. Yafooz

  2. University of Huddersfield, Huddersfield, UK

    Hussain Al-Aqrabi

  3. University Technology Malaysia(UTM), Skudai, Johor, Malaysia

    Arafat Al-Dhaqm

  4. Department of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia

    Abdelhamid Emara

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Al-kaaf, H.A. (2023). Machine Learning Approaches for Kids’ E-learning Monitoring. In: Yafooz, W.M.S., Al-Aqrabi, H., Al-Dhaqm, A., Emara, A. (eds) Kids Cybersecurity Using Computational Intelligence Techniques. Studies in Computational Intelligence, vol 1080. Springer, Cham. https://doi.org/10.1007/978-3-031-21199-7_2

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